3,423 research outputs found
3D detection of roof sections from a single satellite image and application to LOD2-building reconstruction
Reconstructing urban areas in 3D out of satellite raster images has been a
long-standing and challenging goal of both academical and industrial research.
The rare methods today achieving this objective at a Level Of Details rely
on procedural approaches based on geometry, and need stereo images and/or LIDAR
data as input. We here propose a method for urban 3D reconstruction named
KIBS(\textit{Keypoints Inference By Segmentation}), which comprises two novel
features: i) a full deep learning approach for the 3D detection of the roof
sections, and ii) only one single (non-orthogonal) satellite raster image as
model input. This is achieved in two steps: i) by a Mask R-CNN model performing
a 2D segmentation of the buildings' roof sections, and after blending these
latter segmented pixels within the RGB satellite raster image, ii) by another
identical Mask R-CNN model inferring the heights-to-ground of the roof
sections' corners via panoptic segmentation, unto full 3D reconstruction of the
buildings and city. We demonstrate the potential of the KIBS method by
reconstructing different urban areas in a few minutes, with a Jaccard index for
the 2D segmentation of individual roof sections of and on
our two data sets resp., and a height's mean error of such correctly segmented
pixels for the 3D reconstruction of m and m on our two data sets
resp., hence within the LOD2 precision range
Smart Cities: Inverse Design of 3D Urban Procedural Models with Traffic and Weather Simulation
Urbanization, the demographic transition from rural to urban, has changed how we envision and share the world. From just one-fourth of the population living in cities one hundred years ago, now more than half of the population does, and this ratio is expected to grow in the near future. Creating more sustainable, accessible, safe, and enjoyable cities has become an imperative
AutoEncoding Tree for City Generation and Applications
City modeling and generation have attracted an increased interest in various
applications, including gaming, urban planning, and autonomous driving. Unlike
previous works focused on the generation of single objects or indoor scenes,
the huge volumes of spatial data in cities pose a challenge to the generative
models. Furthermore, few publicly available 3D real-world city datasets also
hinder the development of methods for city generation. In this paper, we first
collect over 3,000,000 geo-referenced objects for the city of New York, Zurich,
Tokyo, Berlin, Boston and several other large cities. Based on this dataset, we
propose AETree, a tree-structured auto-encoder neural network, for city
generation. Specifically, we first propose a novel Spatial-Geometric Distance
(SGD) metric to measure the similarity between building layouts and then
construct a binary tree over the raw geometric data of building based on the
SGD metric. Next, we present a tree-structured network whose encoder learns to
extract and merge spatial information from bottom-up iteratively. The resulting
global representation is reversely decoded for reconstruction or generation. To
address the issue of long-dependency as the level of the tree increases, a Long
Short-Term Memory (LSTM) Cell is employed as a basic network element of the
proposed AETree. Moreover, we introduce a novel metric, Overlapping Area Ratio
(OAR), to quantitatively evaluate the generation results. Experiments on the
collected dataset demonstrate the effectiveness of the proposed model on 2D and
3D city generation. Furthermore, the latent features learned by AETree can
serve downstream urban planning applications
Evaluation of non-cost factors affecting the life cycle cost: an exploratory study
Purpose: This paper aims to identify the main non-cost factors affecting accurate estimation of life cycle cost (LCC) in building projects. Design/methodology/approach: Ten factors affecting LCC in building project cost estimates are identified through literature and interviews. A questionnaire survey is conducted to rank these factors in order of priority and provide the views of cost practitioners about the significance of these factors in the accurate estimation of LCC. The data from 138 construction building projects completed in UK were collected and analysed via multiple regression to discover the relationship between capital and LCCs and between non-cost factors and cost estimation at each stage of the life cycle (capital, operation, maintenance and LCC). Findings: The results of analysis of existing LCC data of completing project and survey data from cost professionals are mostly consistent with many literature views and provide a reasonable description of the non-cost factors affecting the accuracy of estimates. Originality/value: The value of this study is in the method used, which involves analysis of existing life data and survey data from cost professionals. The results provide a plausible description of the non-cost factors affecting the accuracy of estimates
Generating descriptive text from functional brain images
Recent work has shown that it is possible to take brain images of a subject acquired while they saw a scene and reconstruct an approximation of that scene from the images. Here we show that it is also possible to generate _text_ from brain images. We began with images collected as participants read names of objects (e.g., ``Apartment'). Without accessing information about the object viewed for an individual image, we were able to generate from it a collection of semantically pertinent words (e.g., "door," "window"). Across images, the sets of words generated overlapped consistently with those contained in articles about the relevant concepts from the online encyclopedia Wikipedia. The technique described, if developed further, could offer an important new tool in building human computer interfaces for use in clinical settings
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